We compare our method with the state-of-the-art MRI reconstruction methods, including Unet[1]}, DCCNN [2]}, ISTA [3]}, CDDN [4]}, PD [5]}, DuDoR [6]}, and OctConv [7]}. Quantitative results are shown in Table REF . We evaluate our proposed CLGNet under \(4\times \)  and \(8\times \)  acceleration on CC-359 Brain dataset and FastMRI dataset, respectively. It can be seen that our proposed CLGNet achieves remarkable improvements over these state-of-the-art methods on different datasets and acceleration rates. This substantiates the flexibility and generality of our proposed method on different organs and different acceleration rates. In addition, Table. REF  shows the comparison results of parameters, Flops, and running-time. The running-time is an average time, which is calculated on 100 images of \(512 \times 512\)  size. Compared with DudoR [6]} and OctConv [7]}, although our proposed CLGNet increases parameters, the running-time and Flops are significantly lower than these two methods. Compared with CDDN [4]}, although our proposed CLGNet increases parameters and Flops, the proposed CLGNet achieves a better performance in less time. In addition, compared with ISTA [3]} and PD [5]}, the CLGNet achieves remarkable improvements in fewer Flops. Therefore, the CLGNet better balances the trade-off between parameters, Flops, running-time, and performance than these top-performing methods.
